A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
Interest in studying Massive Online Open Courses (MOOC) learners’ sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/5/3039 |
| _version_ | 1797615738206289920 |
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| author | Luciano Hidalgo Jorge Munoz-Gama |
| author_facet | Luciano Hidalgo Jorge Munoz-Gama |
| author_sort | Luciano Hidalgo |
| collection | DOAJ |
| description | Interest in studying Massive Online Open Courses (MOOC) learners’ sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. The goal of this research is to provide a domain-driven top-down method that enables educators who are unfamiliar with data and process analytics to search for a set of preset high-level concepts in their own MOOC data, hence simplifying the use of typical process mining techniques. This is accomplished by defining a three-stage process that generates a low-level event log from a minimum data model and then abstracts it to a high-level event log with seven possible learning dynamics that a student may perform in a session. By examining the actions of students who successfully completed a Coursera introductory programming course, the framework was tested. As a consequence, patterns in the repetition of content and assessments were described; it was discovered that students’ willingness to evaluate themselves increases as they advance through the course; and four distinct session types were characterized via clustering. This study shows the potential of employing event abstraction strategies to gain relevant insights from educational data. |
| first_indexed | 2024-03-11T07:30:56Z |
| format | Article |
| id | doaj.art-a2f76709f6dc474fb516a1285e0b039b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T07:30:56Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a2f76709f6dc474fb516a1285e0b039b2023-11-17T07:18:27ZengMDPI AGApplied Sciences2076-34172023-02-01135303910.3390/app13053039A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event AbstractionLuciano Hidalgo0Jorge Munoz-Gama1Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago 8331150, ChileDepartment of Computer Science, Pontificia Universidad Católica de Chile, Santiago 8331150, ChileInterest in studying Massive Online Open Courses (MOOC) learners’ sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. The goal of this research is to provide a domain-driven top-down method that enables educators who are unfamiliar with data and process analytics to search for a set of preset high-level concepts in their own MOOC data, hence simplifying the use of typical process mining techniques. This is accomplished by defining a three-stage process that generates a low-level event log from a minimum data model and then abstracts it to a high-level event log with seven possible learning dynamics that a student may perform in a session. By examining the actions of students who successfully completed a Coursera introductory programming course, the framework was tested. As a consequence, patterns in the repetition of content and assessments were described; it was discovered that students’ willingness to evaluate themselves increases as they advance through the course; and four distinct session types were characterized via clustering. This study shows the potential of employing event abstraction strategies to gain relevant insights from educational data.https://www.mdpi.com/2076-3417/13/5/3039event abstractionMOOClearning dynamicsprocess miningin-session behavior |
| spellingShingle | Luciano Hidalgo Jorge Munoz-Gama A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction Applied Sciences event abstraction MOOC learning dynamics process mining in-session behavior |
| title | A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction |
| title_full | A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction |
| title_fullStr | A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction |
| title_full_unstemmed | A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction |
| title_short | A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction |
| title_sort | domain driven framework to analyze learning dynamics in moocs through event abstraction |
| topic | event abstraction MOOC learning dynamics process mining in-session behavior |
| url | https://www.mdpi.com/2076-3417/13/5/3039 |
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